- HDR: HIGH DYNAMIC RANGE PHOTOGRAPHY -

High dynamic range (HDR) images enable photographers to record a greater
range of tonal detail than a given camera could capture in a single photo.
This opens up a whole new set of lighting possibilities which one might have
previously avoided—for purely technical reasons. The new "merge to HDR"
feature of Photoshop allows the photographer to combine a series of bracketed
exposures into a single image which encompasses the tonal detail of the entire
series. There is no free lunch however; trying to broaden the tonal range
will inevitably come at the expense of decreased contrast in some tones.
Learning to use the merge to HDR feature in Photoshop can help you make the
most of your
dynamic range under tricky lighting—while still balancing this trade-off
with contrast.

MOTIVATION: THE DYNAMIC RANGE DILEMMA

As digital sensors attain progressively higher resolutions, and thereby successively
smaller pixel sizes, the one quality of an image which does not benefit is its
dynamic range. This is particularly apparent in compact cameras with resolutions
near 8 megapixels, as these are more susceptible than ever to blown highlights
or noisy shadow detail. Further, some scenes simply contain a greater
brightness range than can be captured by current digital cameras-- of any type.

The "bright side" is that nearly any camera can actually capture a vast dynamic
range-- just not in a single photo. By varying the shutter speed alone,
most digital cameras can change how much light they let in by a factor of 50,000
or more. High dynamic range imaging attempts to utilize this characteristic
by creating images composed of multiple exposures, which can far surpass the
dynamic range of a single exposure.

WHEN TO USE HDR IMAGES

I would suggest only using HDR images when the scene's brightness distribution
can no longer be easily blended using a graduated neutral density (GND) filter.
This is because GND filters extend dynamic range while still maintaining local
contrast. Scenes which are ideally suited for GND filters are those with
simple lighting geometries, such as the linear blend from dark to light encountered
commonly in landscape photography (corresponding to the relatively dark land
transitioning into bright sky).

GND Filter

Final Result

In contrast, a scene whose brightness distribution is no longer easily blended
using a GND filter is the doorway scene shown below.

Brightness Distribution

Underexposure

Overexposure

We note that the above scene contains roughly three tonal regions with abrupt
transitions at their edges-- therefore requiring a custom-made GND filter.
If we were to look at this in person, we would be able to discern detail both
inside and outside the doorway, because our eyes would adjust to changing brightness.
The goal of HDR use in this article is to better approximate what we
would see with our own eyes through the use of a technique called tonal mapping.

INNER WORKINGS OF AN HDR FILE

Photoshop creates an HDR file by using the EXIF information from each of
your bracketed images to determine their shutter speed, aperture and ISO settings.
It then uses this information to assess how much light came from each image
region. Since this light may vary greatly in its intensity, Photoshop
creates the HDR file using 32-bits to describe each color channel (as opposed
to the usual 16 or 8-bits, as discussed in the tutorial on "Understanding
Bit Depth"). The real benefit is that HDR files use these extra bits
to create a relatively open-ended brightness scale, which can adjust to fit
the needs of your image. The important distinction is that these extra
bits are used differently than the extra bits in 16-bit images, which instead
just define tones more precisely (see tutorials on the "RAW
File Format" and "Posterization").
We refer to the usual 8 and 16-bit files as being low dynamic range (LDR) images,
relatively speaking.

The 32-bit HDR file format describes a greater dynamic range by using
its bits to specify floating point numbers, also referred to as exponential
notation. A floating point number is composed of a decimal number between
1 and 10 multiplied by some power of 10, such as 5.467x103, as opposed
to the usual 0-255 (for 8-bit) or 0-65535 (for 16-bit) integer color specifications.
This way, an image file can specify a brightness of 4,300,000,000 simply as
4.3x109, which would be too large even with 32-bit integers.

We see that the floating point notation certainly looks neater and more concise,
but how does this help a computer? Why not just keep adding more bits
to specify successively larger numbers, and therefore a larger dynamic range?
Recall that for ordinary LDR files, far more bits are used to distinguish lighter
tones than darker tones (from the tutorial on gamma correction, tonal levels
and exposure - to be added). As a result, as more bits are added, an exponentially
greater fraction of these bits are used to specify color more precisely, instead
of extending dynamic range.

Representation of How Bits Are Allocated for
Increasing Brightness

Note: Above representation is qualitative, and depends on
other factors such as screen bit depth, monitor gamma, etc. The more
closely spaced bits for brighter values is a result of the fact that ordinary
8 and 16-bit JPEG files are gamma-encoded, which can actually help increase
dynamic range for low-bit files; gamma-encoding just becomes more and more
inefficient as the bit depth increases.

HDR files get around this LDR dilemma of diminishing returns by using floating
point numbers which are proportional to the actual brightness values of the
subject matter (gamma equals one, or linear). This ensures that bits are
equally spaced throughout the dynamic range, and not just concentrated in the
brighter tones-- allowing for greater bit efficiency. Further, the use
of floating point numbers ensure that all tones are recorded with the same relative
precision, since numbers such as 2.576x103 and 8.924x109
each have the same number of significant figures (four), even though the second
number is more than a million times larger.

Note: just as how using high
bit depth images do not necessarily
mean your image contains more color, a high dynamic range file does not guarantee
greater dynamic range unless this is also present in the actual subject matter.

All of these extra bits provided by the HDR format are great, and effectively
allow for a nearly infinite brightness range to be described. The problem
is that your computer display (or the final photographic print) can only show
a fixed brightness scale. This tutorial therefore focuses on how to create
and convert HDR files into an ordinary 8 or 16-bit image, which can be displayed
on a monitor, or will look great as a photographic print. This process
is also commonly referred to as tonal mapping.

IN-FIELD PREPARATION

Since creating a HDR image requires capturing a series of identically-positioned
exposures, a sturdy tripod is essential. Photoshop has a feature which
attempts to align the images when the camera may have moved between shots, however
best results are achieved when this is not relied upon.

Make sure to take at least three exposures, although five or more
is recommended for optimum accuracy. More exposures allow the HDR algorithm
to better approximate how your camera translates light into digital values (a.k.a.
the digital sensor's response curve)-- creating a more even tonal distribution.
The doorway example is best-suited with several intermediate exposures, in addition
to the two shown previously.

Reference

-1 Stops

-2 Stops

-3 Stops

It is essential that the darkest of these exposures includes no blown highlights
in areas where you want to capture detail. The brightest exposure should
show the darkest regions of the image with enough brightness that they are relatively
noise-free and clearly visible. Each exposure should be separated by one
to two stops, and these are ideally set by varying the shutter speed (as opposed
to aperture or ISO speed). Recall that each "stop" refers to a doubling
(+1 stop) or halving (-1 stop) of the light captured from an exposure.

We also note another disadvantage of HDR images: they require relatively
static subject matter, due to the necessity of several separate exposures.
Our previous ocean sunset example would therefore not be well-suited for the
HDR technique, as the waves would have moved significantly between each exposure.

CREATING A 32-BIT HDR FILE IN PHOTOSHOP

Here we use Adobe Photoshop to convert the sequence of exposures into a single
image, which uses tonal mapping to approximate what we would see with our eye.
Before tonal mapping can be performed, we first need to combine all exposures
into a single 32-bit HDR file.

Open the HDR tool (File>Automate>Merge to HDR�), and load all photographs
in the exposure sequence; for this example it would be the four images shown
in the previous section. If your images were not taken on a stable tripod,
this step may require checking "Attempt to Automatically Align Source Images"
(which greatly increases processing time). After pressing OK, you will
soon see a "Computing Camera Response Curves" message.

Once your computer has stopped processing, it will show a window with their
combined histogram. Photoshop has estimated the white point, but this
value often clips the highlights. You may wish to move the white point
slider to the rightmost edge of the histogram peaks in order to see all highlight
detail. This value is for preview purposes only and will require setting
more precisely later. After pressing OK, this leaves you with a 32-bit
HDR image, which can now be saved if required. Note how the image may
still appear quite dark; only once it has been converted into a 16 or 8-bit
image (using tonal mapping) will it begin to look more like the desired result.

At this stage, very few image processing functions can be applied to a 32-bit
HDR file, so it is of little use other than for archival purposes. One
function which is available is exposure adjustment (Image>Adjustments>Exposure).
You may wish to try increasing the exposure to see any hidden shadow detail,
or decreasing the exposure to see any hidden highlight detail.

USING HDR TONAL MAPPING IN PHOTOSHOP

Here we use Adobe Photoshop to convert the 32-bit HDR image into a 16 or
8-bit LDR file using tonal mapping. This requires interpretive decisions
about the type of tonal mapping, depending on the subject matter and brightness
distribution within the photograph.

Convert into a regular 16-bit image (Image>Mode>16 Bits/Channel�) and you
will see the HDR Conversion tool. The tonal mapping method can be chosen
from one of four options, described below.

Exposure and Gamma

This method lets you manually adjust
the exposure and gamma, which serve as the equivalent to brightness
and contrast adjustment, respectively.

Highlight Compression

This method has no options and applies
a custom
tonal curve, which greatly reduces highlight contrast in order
to brighten and restore contrast in the rest of the image.

Equalize Histogram

This method attempts to redistribute
the HDR histogram into the contrast range of a normal 16 or 8-bit
image. This uses a custom
tonal curve which spreads out histogram peaks so that the histogram
becomes more homogenous. It generally works best for image
histograms which have several relatively narrow peaks with no pixels
in between.

Local Adaptation

This is the most flexible method and
probably the one which is of most use to photographers. Unlike
the other three methods, this one changes how much it brightens
or darkens regions on a per-pixel basis (similar to
local contrast enhancement). This has the effect of tricking
the eye into thinking that the image has more contrast, which is
often critical in contrast-deprived HDR images. This method
also allows changing the tonal curve to better suit the image.

Before using any of the above methods, one may first wish to set the black
and white points on the image histogram sliders (see "Using
Levels in Photoshop" for a background on this concept). Click on the
double arrow next to "Toning Curve and Histogram" to show the image histogram
and sliders.

The remainder of this tutorial focuses on settings related to the "local
adaptation" method, as this is likely the most-used, and provides the greatest
degree of flexibility.

CONCEPT: TONAL HIERARCHY & IMAGE CONTRAST

In contrast to the other three conversion methods, the local adaptation method
does not necessarily retain the overall hierarchy of tones. It translates
pixel intensities not just with a single tonal curve, but instead also based
on the surrounding pixel values. This means that unlike using a tonal
curve, tones on the histogram are not just stretched and compressed, but may
instead cross positions. Visually, this would mean that some part of the
subject matter which was initially darker than some other part could later acquire
the same brightness or become lighter than that other part-- if even by a small
amount.

Underexposed Photo

Overexposed Photo

Final Composite that Violates Large-Scale Tonal
Hierarchy

A clear example where global tonal hierarchy is not violated is the example
used in the page on
using a GND to extend dynamic range (although this is not how local adaptation
works). In this example, even though the foreground sea foam and rock
reflections are actually darker than the distant ocean surface, the final image
renders the distant ocean as being darker. The key concept here is
that over larger image regions our eyes adjust to changing brightness (such
as looking up at a bright sky), while over smaller distances our eyes do not.
Mimicking this characteristic of vision can be thought of as a goal of the local
adaptive method-- particularly for brightness distributions which are more complex
than the simple vertical blend in the ocean sunset above.

An example of a more complex brightness distribution is shown below for three
statue images. We refer to contrast over larger image distances as global
contrast, whereas contrast changes over smaller image distances are termed local
contrast. The local adaptation method attempts to maintain local contrast,
while decreasing global contrast (similar to that performed with the ocean sunset
example).

Original Image

High Global Contrast
Low Local Contrast

Low Global Contrast
High Local Contrast

The above example illustrates visually how local and global contrast impact
an image. Note how the large-scale (global) patches of light and dark
are exaggerated for the case of high global contrast. Conversely, for
the case of low global contrast the front of the statue's face is virtually
the same brightness as it's side.

The original image looks fine since all tonal regions are clearly visible,
and shown with sufficient contrast to give it a three-dimensional appearance.
Now imagine that we started with the middle image, which would be an ideal candidate
for HDR conversion. Tonal mapping using local adaptation would likely
produce an image similar to the far right image (although perhaps not as exaggerated),
since it retains local contrast while still decreasing global contrast (thereby
retaining texture in the darkest and lightest regions).

HDR CONVERSION USING LOCAL ADAPTATION

The distance which distinguishes between local and global contrast is set
using the radius value. Radius and threshold are similar to the settings
for an
unsharp mask used for
local
contrast enhancement. A high threshold improves local contrast, but
also risks inducing halo artifacts, whereas too low of a radius can make the
image appear washed out. For any given image, it is recommended to adjust
each of these to see their effect, since their ideal combination varies depending
on image content.

In addition to the radius and threshold values, images almost always require
adjustments to the tonal curve. This technique is identical to that described
in the
Photoshop curves tutorial, where small and gradual changes in the curve's
slope are nearly always ideal. This curve is shown for our doorway example
below, yielding the final result.

Photoshop CS2 Tool

Final Result
Using Local Adaptation Method

HDR images which have been converted into 8 or 16-bit often require touching
up in order to improve their color accuracy. Subtle use of levels and
saturation can drastically improve problem areas in the image. In general,
regions which have increased in contrast (a large slope in the tonal curve)
will exhibit an increase in color saturation, whereas the opposite occurs for
a decrease in contrast. Changes in saturation may sometimes be desirable
when brightening shadows, but in most other instances this should be avoided.

The main problem with the local adaptation method is that it cannot distinguish
between incident and reflected light. As a result, it may unnecessarily
darken naturally white textures and brighten darker ones. Be aware of
this when choosing the radius and threshold settings so that this effect can
be minimized.

TIP: USING HDR TO REDUCE SHADOW NOISE

Even if your scene does not require more dynamic range, your final photo
may still improve from a side benefit: decreased shadow noise. Ever noticed
how digital images always have more noise in the shadows than in brighter tones?
This is because the
image's signal to noise ratio is higher where the image has collected more
of a light signal. You can take advantage of this by combining a properly
exposed image with one which has been overexposed. Photoshop always uses
the most exposed image to represent a given tone—thereby collecting more light
in the shadow detail (but without overexposing).

RECOMMENDATIONS

Keep in mind that HDR images are extremely new-- particularly in the field
of digital photography. Existing tools are therefore likely to improve
significantly; there is not currently, and may never be, an automated single-step
process which converts all HDR images into those which look pleasing on screen,
or in a print. Good HDR conversions therefore require significant work
and experimentation in order to achieve realistic and pleasing final images.

Additionally, incorrectly converted or problematic HDR images may appear
washed out after conversion. While re-investigating the conversion settings
is recommended as the first corrective step, touch-up with local contrast enhancement
may also yield a more pleasing result.

As with all new tools, be careful not to overdo their use. Use care
when violating the image�s original tonal hierarchy; do not expect deep shadows
to become nearly as light as a bright sky. In our doorway example, the
sunlit building and sky are the brightest objects, and they stayed that way
in our final image. Overdoing editing during HDR conversion easily can
cause the image to lose its sense of realism. Furthermore, HDR should
only be used when necessary; best results can always be achieved by having good
lighting to begin with.

Note: To clarify email queries, no photo within my gallery used
the HDR technique. Only when necessary, I prefer to use linear and radial graduated
neutral density filters to control drastically varying light. If used properly,
these do not induce halo artifacts while still maintaining local contrast. Further,
these have been a standard by landscape photographers for nearly a century.
In some situations, however, I can certainly see when the photo would be unattainable
without HDR.